Qwen: Qwen3 235B A22B Instruct 2507 FP8 Throughput
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Qwen3 235B A22B Instruct 2507 FP8 Throughput is a multilingual mixture-of-experts model from Alibaba's Qwen team, with 235B total parameters and 22B active per token. This non-thinking, instruction-tuned variant is tuned for fast, direct responses across reasoning, math, coding, and general knowledge.
It posts strong published benchmarks, including 77.5% on GPQA, 70.3% on AIME25, 51.8% on LiveCodeBench v6, and 79.2% on Arena-Hard v2, with results that rival or exceed peers like GPT-4o and DeepSeek-V3 on math and coding tasks.
With a 256K-token context window and solid tool-calling support, it's a strong, cost-effective pick for developers building agentic, long-context, and multilingual applications.
Context Window 262K
tokens
Max Output 262K
tokens
Input Cost $0.2
per million tokens
Output Cost $0.6
per million tokens
Input text
modalities
Tool Use Yes
Release Date Jul 22, 2025
Code Example
Add AI to your app with the Puter.js AI API — no API keys or setup required.
// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms").then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms").then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
More AI Models From Qwen
Qwen3.8 Flash
Qwen3.8 Flash is a multimodal model from Alibaba's Qwen team, released August 26, 2026, as the fast, lower-cost tier of the Qwen3.8 family alongside Qwen3.8 Max and Qwen3.8 27B. It uses a mixture-of-experts architecture with 125B total parameters and 6B active per token, an early preview of the architecture planned for Qwen4. It accepts text, image, and video input and returns text, with a 1,000,000 token context window and output capped at 128,000 tokens. The API supports tool calling, structured outputs via JSON schema, and prompt caching, with cached input billed at $0.016 per million tokens. Pricing is $0.14 per million input tokens and $0.42 per million output tokens, about one-twelfth the cost of Qwen3.8 Max. Alibaba says it was trained at roughly one-ninth the cost of Qwen3.7-Plus and reports higher scores on benchmarks including SWE-bench Pro and CoWorkBench, an agentic office-task benchmark.
ChatQwen3.8 27B
Qwen3.8 27B is a dense, open-weight multimodal model from Alibaba's Qwen team, released August 14, 2026 as a smaller member of the Qwen3.8 family alongside the flagship Qwen3.8 Max. It combines Gated DeltaNet linear attention with standard gated attention across 64 layers, giving a 27 billion parameter dense model a native 262K token context window, extendable to 1M tokens. It accepts text, image, and video input, including hour-scale video and STEM diagrams. Alibaba reports 61.7 on SWE-bench Pro and 73.0 on Terminal Bench 2.1, both improvements over the earlier Qwen3.6 27B, and 89.2 on GPQA Diamond. Released under Apache 2.0, it gives developers an open-weight alternative to Qwen3.8 Max for coding and agentic tasks, at a fraction of the parameter count.
ChatQwen3.8 2.4T A95B
Qwen3.8 2.4T A95B is Alibaba's open-weight release of its Qwen3.8 Max flagship, a sparse mixture-of-experts model with 2.4 trillion total parameters and 95 billion active per token, routed across 512 experts. It uses a hybrid attention design (Gated DeltaNet and Gated Attention layers) across 92 layers, with a native 262K context window and thinking mode enabled for every response. Alibaba reports 93.0 on PaperBench (ahead of GPT-5.6 Sol's 90.5), 92.6 on GPQA Diamond, 86.6 on Terminal-Bench 2.1, and 67.7 on SWE-bench Pro, positioning it for coding, research, and long-horizon agentic work. It gives developers access to Qwen-Max-class capability under open weights, useful for teams that want frontier-level coding and agentic performance without a closed API.
Frequently Asked Questions
You can access Qwen3 235B A22B Instruct 2507 FP8 Throughput by Qwen through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.
Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add Qwen3 235B A22B Instruct 2507 FP8 Throughput to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.
| Price per 1M tokens | |
|---|---|
| Input | $0.2 |
| Output | $0.6 |
Qwen3 235B A22B Instruct 2507 FP8 Throughput was created by Qwen and released on Jul 22, 2025.
Qwen3 235B A22B Instruct 2507 FP8 Throughput supports a context window of 262K tokens. For reference, that is roughly equivalent to 524 pages of text.
Qwen3 235B A22B Instruct 2507 FP8 Throughput can generate up to 262K tokens in a single response.
Qwen3 235B A22B Instruct 2507 FP8 Throughput accepts the following input types: text. It produces: text.
Yes, Qwen3 235B A22B Instruct 2507 FP8 Throughput supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
Yes — the Qwen3 235B A22B Instruct 2507 FP8 Throughput API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
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